add app

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,5 @@
+gradio_cached_examples/
+requirements-dev.lock
+
+__pycache__/
+*.py[cod]

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Lagon Technologies
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,13 +1,21 @@
 ---
 title: Finegrain Object Cutter
-emoji: 🔥
+emoji: ✂️
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
 sdk_version: 4.42.0
-app_file: app.py
+app_file: src/app.py
 pinned: false
 license: mit
+short_description: Create high-quality HD cutouts with just a text prompt
+tags:
+- refiners
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Finegrain Object Cutter
+
+## Links
+
+- https://github.com/finegrain-ai/refiners
+- https://finegrain.ai/

pyproject.toml

@@ -0,0 +1,56 @@
+[project]
+name = "cutter"
+version = "0.1.0"
+description = "Finegrain Object Cutter (Gradio)"
+authors = [
+    { name = "Pierre Chapuis", email = "pierre@lagon.tech" }
+]
+dependencies = [
+    "gradio>=4.42.0",
+    "gradio-image-annotation>=0.2.3",
+    "pillow>=10.4.0",
+    "pillow-heif>=0.18.0",
+    "refiners @ git+https://github.com/finegrain-ai/refiners",
+    "numba>=0.60.0",
+    "pymatting>=1.1.12",
+    "transformers>=4.44.2",
+    "spaces>=0.29.3",
+    "numpy<2.0.0",
+    "gradio-imageslider @ https://fg-cwl-public.s3.eu-west-1.amazonaws.com/9zlqqobl/gradio_imageslider-0.0.20-py3-none-any.whl",
+]
+readme = "README.md"
+requires-python = ">= 3.12, <3.13"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.rye]
+managed = true
+dev-dependencies = []
+
+[tool.hatch.metadata]
+allow-direct-references = true
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/cutter"]
+
+[tool.ruff]
+line-length = 120
+target-version = "py312"
+
+[tool.ruff.lint]
+select = [
+  "E",   # pycodestyle errors
+  "W",   # pycodestyle warnings
+  "F",   # pyflakes
+  "UP",  # pyupgrade
+  "A",   # flake8-builtins
+  "B",   # flake8-bugbear
+  "Q",   # flake8-quotes
+  "I",   # isort
+]
+
+[tool.pyright]
+include = ["src"]
+exclude = ["**/__pycache__"]

requirements.lock

@@ -0,0 +1,267 @@
+# generated by rye
+# use `rye lock` or `rye sync` to update this lockfile
+#
+# last locked with the following flags:
+#   pre: false
+#   features: []
+#   all-features: false
+#   with-sources: false
+#   generate-hashes: false
+#   universal: false
+
+-e file:.
+aiofiles==23.2.1
+    # via gradio
+annotated-types==0.7.0
+    # via pydantic
+anyio==4.4.0
+    # via gradio
+    # via httpx
+    # via starlette
+build==1.2.1
+    # via gradio-imageslider
+certifi==2024.7.4
+    # via httpcore
+    # via httpx
+    # via requests
+charset-normalizer==3.3.2
+    # via requests
+click==8.1.7
+    # via typer
+    # via uvicorn
+contourpy==1.3.0
+    # via matplotlib
+cycler==0.12.1
+    # via matplotlib
+fastapi==0.112.2
+    # via gradio
+ffmpy==0.4.0
+    # via gradio
+filelock==3.15.4
+    # via huggingface-hub
+    # via torch
+    # via transformers
+    # via triton
+fonttools==4.53.1
+    # via matplotlib
+fsspec==2024.6.1
+    # via gradio-client
+    # via huggingface-hub
+    # via torch
+gradio==4.42.0
+    # via cutter
+    # via gradio-image-annotation
+    # via gradio-imageslider
+    # via spaces
+gradio-client==1.3.0
+    # via gradio
+gradio-image-annotation==0.2.3
+    # via cutter
+gradio-imageslider @ https://fg-cwl-public.s3.eu-west-1.amazonaws.com/9zlqqobl/gradio_imageslider-0.0.20-py3-none-any.whl
+    # via cutter
+h11==0.14.0
+    # via httpcore
+    # via uvicorn
+httpcore==1.0.5
+    # via httpx
+httpx==0.27.2
+    # via gradio
+    # via gradio-client
+    # via spaces
+huggingface-hub==0.24.6
+    # via gradio
+    # via gradio-client
+    # via tokenizers
+    # via transformers
+idna==3.8
+    # via anyio
+    # via httpx
+    # via requests
+importlib-resources==6.4.4
+    # via gradio
+jaxtyping==0.2.33
+    # via refiners
+jinja2==3.1.4
+    # via gradio
+    # via torch
+kiwisolver==1.4.5
+    # via matplotlib
+llvmlite==0.43.0
+    # via numba
+markdown-it-py==3.0.0
+    # via rich
+markupsafe==2.1.5
+    # via gradio
+    # via jinja2
+matplotlib==3.9.2
+    # via gradio
+mdurl==0.1.2
+    # via markdown-it-py
+mpmath==1.3.0
+    # via sympy
+networkx==3.3
+    # via torch
+numba==0.60.0
+    # via cutter
+    # via pymatting
+numpy==1.26.4
+    # via contourpy
+    # via cutter
+    # via gradio
+    # via matplotlib
+    # via numba
+    # via pandas
+    # via pymatting
+    # via refiners
+    # via scipy
+    # via transformers
+nvidia-cublas-cu12==12.1.3.1
+    # via nvidia-cudnn-cu12
+    # via nvidia-cusolver-cu12
+    # via torch
+nvidia-cuda-cupti-cu12==12.1.105
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.1.105
+    # via torch
+nvidia-cuda-runtime-cu12==12.1.105
+    # via torch
+nvidia-cudnn-cu12==9.1.0.70
+    # via torch
+nvidia-cufft-cu12==11.0.2.54
+    # via torch
+nvidia-curand-cu12==10.3.2.106
+    # via torch
+nvidia-cusolver-cu12==11.4.5.107
+    # via torch
+nvidia-cusparse-cu12==12.1.0.106
+    # via nvidia-cusolver-cu12
+    # via torch
+nvidia-nccl-cu12==2.20.5
+    # via torch
+nvidia-nvjitlink-cu12==12.6.20
+    # via nvidia-cusolver-cu12
+    # via nvidia-cusparse-cu12
+nvidia-nvtx-cu12==12.1.105
+    # via torch
+orjson==3.10.7
+    # via gradio
+packaging==24.1
+    # via build
+    # via gradio
+    # via gradio-client
+    # via huggingface-hub
+    # via matplotlib
+    # via refiners
+    # via spaces
+    # via transformers
+pandas==2.2.2
+    # via gradio
+pillow==10.4.0
+    # via cutter
+    # via gradio
+    # via gradio-imageslider
+    # via matplotlib
+    # via pillow-heif
+    # via pymatting
+    # via refiners
+pillow-heif==0.18.0
+    # via cutter
+psutil==5.9.8
+    # via spaces
+pydantic==2.8.2
+    # via fastapi
+    # via gradio
+    # via spaces
+pydantic-core==2.20.1
+    # via pydantic
+pydub==0.25.1
+    # via gradio
+pygments==2.18.0
+    # via rich
+pymatting==1.1.12
+    # via cutter
+pyparsing==3.1.4
+    # via matplotlib
+pyproject-hooks==1.1.0
+    # via build
+python-dateutil==2.9.0.post0
+    # via matplotlib
+    # via pandas
+python-multipart==0.0.9
+    # via gradio
+pytz==2024.1
+    # via pandas
+pyyaml==6.0.2
+    # via gradio
+    # via huggingface-hub
+    # via transformers
+refiners @ git+https://github.com/finegrain-ai/refiners@7ca1774b5f8f172708db647a26c3be68858f285a
+    # via cutter
+regex==2024.7.24
+    # via transformers
+requests==2.32.3
+    # via huggingface-hub
+    # via spaces
+    # via transformers
+rich==13.8.0
+    # via typer
+ruff==0.6.2
+    # via gradio
+safetensors==0.4.4
+    # via refiners
+    # via transformers
+scipy==1.14.1
+    # via pymatting
+semantic-version==2.10.0
+    # via gradio
+setuptools==74.0.0
+    # via torch
+shellingham==1.5.4
+    # via typer
+six==1.16.0
+    # via python-dateutil
+sniffio==1.3.1
+    # via anyio
+    # via httpx
+spaces==0.29.3
+    # via cutter
+starlette==0.38.2
+    # via fastapi
+sympy==1.13.2
+    # via torch
+tokenizers==0.19.1
+    # via transformers
+tomlkit==0.12.0
+    # via gradio
+torch==2.4.0
+    # via refiners
+tqdm==4.66.5
+    # via huggingface-hub
+    # via transformers
+transformers==4.44.2
+    # via cutter
+triton==3.0.0
+    # via torch
+typeguard==2.13.3
+    # via jaxtyping
+typer==0.12.5
+    # via gradio
+typing-extensions==4.12.2
+    # via fastapi
+    # via gradio
+    # via gradio-client
+    # via huggingface-hub
+    # via pydantic
+    # via pydantic-core
+    # via spaces
+    # via torch
+    # via typer
+tzdata==2024.1
+    # via pandas
+urllib3==2.2.2
+    # via gradio
+    # via requests
+uvicorn==0.30.6
+    # via gradio
+websockets==12.0
+    # via gradio-client

requirements.txt

@@ -0,0 +1,10 @@
+gradio_image_annotation==0.2.3
+https://huggingface.co/spaces/finegrain/finegrain-object-cutter/resolve/main/gradio_imageslider-0.0.20-py3-none-any.whl
+pillow>=10.4.0
+pillow-heif>=0.18.0
+git+https://github.com/finegrain-ai/refiners@7ca1774b5f8f172708db647a26c3be68858f285a
+numba>=0.60.0
+pymatting>=1.1.12
+transformers>=4.44.2
+spaces>=0.29.3
+numpy<2.0.0

src/app.py

@@ -0,0 +1,343 @@
+import tempfile
+import time
+from collections.abc import Sequence
+from typing import Any, cast
+
+import gradio as gr
+import numpy as np
+import pillow_heif
+import spaces
+import torch
+from gradio_image_annotation import image_annotator
+from gradio_imageslider import ImageSlider
+from PIL import Image
+from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
+from refiners.fluxion.utils import no_grad
+from refiners.solutions import BoxSegmenter
+from transformers import GroundingDinoForObjectDetection, GroundingDinoProcessor
+
+BoundingBox = tuple[int, int, int, int]
+
+pillow_heif.register_heif_opener()
+pillow_heif.register_avif_opener()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# weird dance because ZeroGPU
+segmenter = BoxSegmenter(device="cpu")
+segmenter.device = device
+segmenter.model = segmenter.model.to(device=segmenter.device)
+
+gd_model_path = "IDEA-Research/grounding-dino-base"
+gd_processor = GroundingDinoProcessor.from_pretrained(gd_model_path)
+gd_model = GroundingDinoForObjectDetection.from_pretrained(gd_model_path, torch_dtype=torch.float32)
+gd_model = gd_model.to(device=device)  # type: ignore
+assert isinstance(gd_model, GroundingDinoForObjectDetection)
+
+
+def bbox_union(bboxes: Sequence[list[int]]) -> BoundingBox | None:
+    if not bboxes:
+        return None
+    for bbox in bboxes:
+        assert len(bbox) == 4
+        assert all(isinstance(x, int) for x in bbox)
+    return (
+        min(bbox[0] for bbox in bboxes),
+        min(bbox[1] for bbox in bboxes),
+        max(bbox[2] for bbox in bboxes),
+        max(bbox[3] for bbox in bboxes),
+    )
+
+
+def corners_to_pixels_format(bboxes: torch.Tensor, width: int, height: int) -> torch.Tensor:
+    x1, y1, x2, y2 = bboxes.round().to(torch.int32).unbind(-1)
+    return torch.stack((x1.clamp_(0, width), y1.clamp_(0, height), x2.clamp_(0, width), y2.clamp_(0, height)), dim=-1)
+
+
+def gd_detect(img: Image.Image, prompt: str) -> BoundingBox | None:
+    assert isinstance(gd_processor, GroundingDinoProcessor)
+
+    # Grounding Dino expects a dot after each category.
+    inputs = gd_processor(images=img, text=f"{prompt}.", return_tensors="pt").to(device=device)
+
+    with no_grad():
+        outputs = gd_model(**inputs)
+    width, height = img.size
+    results: dict[str, Any] = gd_processor.post_process_grounded_object_detection(
+        outputs,
+        inputs["input_ids"],
+        target_sizes=[(height, width)],
+    )[0]
+    assert "boxes" in results and isinstance(results["boxes"], torch.Tensor)
+
+    bboxes = corners_to_pixels_format(results["boxes"].cpu(), width, height)
+    return bbox_union(bboxes.numpy().tolist())
+
+
+def apply_mask(
+    img: Image.Image,
+    mask_img: Image.Image,
+    defringe: bool = True,
+) -> Image.Image:
+    assert img.size == mask_img.size
+    img = img.convert("RGB")
+    mask_img = mask_img.convert("L")
+
+    if defringe:
+        # Mitigate edge halo effects via color decontamination
+        rgb, alpha = np.asarray(img) / 255.0, np.asarray(mask_img) / 255.0
+        foreground = cast(np.ndarray[Any, np.dtype[np.uint8]], estimate_foreground_ml(rgb, alpha))
+        img = Image.fromarray((foreground * 255).astype("uint8"))
+
+    result = Image.new("RGBA", img.size)
+    result.paste(img, (0, 0), mask_img)
+    return result
+
+
+@spaces.GPU
+def _gpu_process(
+    img: Image.Image,
+    prompt: str | BoundingBox | None,
+) -> tuple[Image.Image, BoundingBox | None, list[str]]:
+    # Because of ZeroGPU shenanigans, we need a *single* function with the
+    # `spaces.GPU` decorator that *does not* contain postprocessing.
+
+    time_log: list[str] = []
+
+    if isinstance(prompt, str):
+        t0 = time.time()
+        bbox = gd_detect(img, prompt)
+        time_log.append(f"detect: {time.time() - t0}")
+        if not bbox:
+            print(time_log[0])
+            raise gr.Error("No object detected")
+    else:
+        bbox = prompt
+
+    t0 = time.time()
+    mask = segmenter(img, bbox)
+    time_log.append(f"segment: {time.time() - t0}")
+
+    return mask, bbox, time_log
+
+
+def _process(
+    img: Image.Image,
+    prompt: str | BoundingBox | None,
+) -> tuple[tuple[Image.Image, Image.Image], gr.DownloadButton]:
+    # enforce max dimensions for pymatting performance reasons
+    if img.width > 2048 or img.height > 2048:
+        orig_width = img.width
+        img.thumbnail((2048, 2048))
+        if isinstance(prompt, tuple):
+            x0, y0, x1, y1 = (int(x * 2048 / orig_width) for x in prompt)
+            prompt = (x0, y0, x1, y1)
+
+    mask, bbox, time_log = _gpu_process(img, prompt)
+
+    t0 = time.time()
+    masked_alpha = apply_mask(img, mask, defringe=True)
+    time_log.append(f"crop: {time.time() - t0}")
+    print(", ".join(time_log))
+
+    masked_rgb = Image.alpha_composite(Image.new("RGBA", masked_alpha.size, "white"), masked_alpha)
+
+    thresholded = mask.point(lambda p: 255 if p > 10 else 0)
+    bbox = thresholded.getbbox()
+    to_dl = masked_alpha.crop(bbox)
+
+    temp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    to_dl.save(temp, format="PNG")
+    temp.close()
+
+    return (img, masked_rgb), gr.DownloadButton(value=temp.name, interactive=True)
+
+
+def process_bbox(prompts: dict[str, Any]) -> tuple[tuple[Image.Image, Image.Image], gr.DownloadButton]:
+    assert isinstance(img := prompts["image"], Image.Image)
+    assert isinstance(boxes := prompts["boxes"], list)
+    if len(boxes) == 1:
+        assert isinstance(box := boxes[0], dict)
+        bbox = tuple(box[k] for k in ["xmin", "ymin", "xmax", "ymax"])
+    else:
+        assert len(boxes) == 0
+        bbox = None
+    return _process(img, bbox)
+
+
+def on_change_bbox(img: Image.Image | None):
+    return gr.update(interactive=bool(img))
+
+
+def process_prompt(img: Image.Image, prompt: str) -> tuple[tuple[Image.Image, Image.Image], gr.DownloadButton]:
+    return _process(img, prompt)
+
+
+def on_change_prompt(img: Image.Image | None, prompt: str | None):
+    return gr.update(interactive=bool(img and prompt))
+
+
+TITLE = """
+<center>
+
+  <h1 style="font-size: 1.5rem; margin-bottom: 0.5rem;">
+    Object Cutter Powered By Refiners
+  </h1>
+
+  <div style="
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 0.5rem;
+    margin-bottom: 0.5rem;
+    font-size: 1.25rem;
+    flex-wrap: wrap;
+  ">
+    <a href="https://github.com/finegrain-ai/refiners" target="_blank">[Refiners]</a>
+    <a href="https://finegrain.ai/" target="_blank">[Finegrain]</a>
+    <a
+        href="https://huggingface.co/spaces/finegrain/finegrain-object-eraser"
+        target="_blank"
+    >[Finegrain Object Eraser]</a>
+    <a
+        href="https://huggingface.co/spaces/finegrain/finegrain-image-enhancer"
+        target="_blank"
+    >[Finegrain Image Enhancer]</a>
+  </div>
+
+  <p>
+    Create high-quality HD cutouts for any object in your image with just a text prompt — no manual work required!
+    <br>
+    The object will be available on a transparent background, ready to paste elsewhere.
+  </p>
+
+  <p>
+    This space uses the
+    <a
+        href="https://huggingface.co/finegrain/finegrain-box-segmenter"
+        target="_blank"
+    >Finegrain Box Segmenter model</a>,
+    trained with a mix of natural data curated by Finegrain and
+    <a
+        href="https://huggingface.co/datasets/Nfiniteai/product-masks-sample"
+        target="_blank"
+    >synthetic data provided by Nfinite</a>.
+    <br>
+    It is powered by Refiners, our open source micro-framework for simple foundation model adaptation.
+    If you enjoyed it, please consider starring Refiners on GitHub!
+  </p>
+
+  <a href="https://github.com/finegrain-ai/refiners" target="_blank">
+    <img src="https://img.shields.io/github/stars/finegrain-ai/refiners?style=social" />
+  </a>
+
+</center>
+"""
+
+with gr.Blocks() as demo:
+    gr.HTML(TITLE)
+
+    with gr.Tab("By prompt", id="tab_prompt"):
+        with gr.Row():
+            with gr.Column():
+                iimg = gr.Image(type="pil", label="Input")
+                prompt = gr.Textbox(label="What should we cut?")
+                btn = gr.ClearButton(value="Cut Out Object", interactive=False)
+            with gr.Column():
+                oimg = ImageSlider(label="Before / After", show_download_button=False, interactive=False)
+                dlbt = gr.DownloadButton("Download Cutout", interactive=False)
+
+        btn.add(oimg)
+
+        for inp in [iimg, prompt]:
+            inp.change(
+                fn=on_change_prompt,
+                inputs=[iimg, prompt],
+                outputs=[btn],
+            )
+        btn.click(
+            fn=process_prompt,
+            inputs=[iimg, prompt],
+            outputs=[oimg, dlbt],
+            api_name=False,
+        )
+
+        examples = [
+            [
+                "examples/potted-plant.jpg",
+                "potted plant",
+            ],
+            [
+                "examples/chair.jpg",
+                "chair",
+            ],
+            [
+                "examples/black-lamp.jpg",
+                "black lamp",
+            ],
+        ]
+
+        ex = gr.Examples(
+            examples=examples,
+            inputs=[iimg, prompt],
+            outputs=[oimg, dlbt],
+            fn=process_prompt,
+            cache_examples=True,
+        )
+
+    with gr.Tab("By bounding box", id="tab_bb"):
+        with gr.Row():
+            with gr.Column():
+                annotator = image_annotator(
+                    image_type="pil",
+                    disable_edit_boxes=True,
+                    show_download_button=False,
+                    show_share_button=False,
+                    single_box=True,
+                    label="Input",
+                )
+                btn = gr.ClearButton(value="Cut Out Object", interactive=False)
+            with gr.Column():
+                oimg = ImageSlider(label="Before / After", show_download_button=False)
+                dlbt = gr.DownloadButton("Download Cutout", interactive=False)
+
+        btn.add(oimg)
+
+        iimg.change(
+            fn=on_change_bbox,
+            inputs=[iimg],
+            outputs=[btn],
+        )
+        btn.click(
+            fn=process_bbox,
+            inputs=[annotator],
+            outputs=[oimg, dlbt],
+            api_name=False,
+        )
+
+        examples = [
+            {
+                "image": "examples/potted-plant.jpg",
+                "boxes": [{"xmin": 51, "ymin": 511, "xmax": 639, "ymax": 1255}],
+            },
+            {
+                "image": "examples/chair.jpg",
+                "boxes": [{"xmin": 98, "ymin": 330, "xmax": 973, "ymax": 1468}],
+            },
+            {
+                "image": "examples/black-lamp.jpg",
+                "boxes": [{"xmin": 88, "ymin": 148, "xmax": 700, "ymax": 1414}],
+            },
+        ]
+
+        ex = gr.Examples(
+            examples=examples,
+            inputs=[annotator],
+            outputs=[oimg, dlbt],
+            fn=process_bbox,
+            cache_examples=True,
+        )
+
+
+demo.queue(max_size=30, api_open=False)
+demo.launch(show_api=False)